Overview

Dataset statistics

Number of variables24
Number of observations99
Missing cells50
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.7 KiB
Average record size in memory193.3 B

Variable types

Numeric12
Categorical12

Alerts

city has constant value "Lahore" Constant
province_name has constant value "Punjab" Constant
purpose has constant value "For Sale" Constant
page_url has a high cardinality: 99 distinct values High cardinality
price is highly correlated with area_marla and 1 other fieldsHigh correlation
longitude is highly correlated with dayHigh correlation
baths is highly correlated with bedroomsHigh correlation
area_marla is highly correlated with price and 1 other fieldsHigh correlation
area_sqft is highly correlated with price and 1 other fieldsHigh correlation
bedrooms is highly correlated with bathsHigh correlation
day is highly correlated with longitudeHigh correlation
location_id is highly correlated with longitude and 1 other fieldsHigh correlation
price is highly correlated with area_marla and 1 other fieldsHigh correlation
latitude is highly correlated with longitudeHigh correlation
longitude is highly correlated with location_id and 2 other fieldsHigh correlation
baths is highly correlated with bedroomsHigh correlation
area_marla is highly correlated with price and 1 other fieldsHigh correlation
area_sqft is highly correlated with price and 1 other fieldsHigh correlation
bedrooms is highly correlated with bathsHigh correlation
year is highly correlated with monthHigh correlation
month is highly correlated with yearHigh correlation
day is highly correlated with location_id and 1 other fieldsHigh correlation
price is highly correlated with area_marla and 1 other fieldsHigh correlation
baths is highly correlated with bedroomsHigh correlation
area_marla is highly correlated with price and 1 other fieldsHigh correlation
area_sqft is highly correlated with price and 1 other fieldsHigh correlation
bedrooms is highly correlated with bathsHigh correlation
location is highly correlated with locality and 10 other fieldsHigh correlation
locality is highly correlated with location and 10 other fieldsHigh correlation
agency is highly correlated with location and 8 other fieldsHigh correlation
price_bin is highly correlated with location and 8 other fieldsHigh correlation
area is highly correlated with location and 7 other fieldsHigh correlation
property_type is highly correlated with location and 6 other fieldsHigh correlation
city is highly correlated with location and 10 other fieldsHigh correlation
purpose is highly correlated with location and 10 other fieldsHigh correlation
province_name is highly correlated with location and 10 other fieldsHigh correlation
agent is highly correlated with location and 8 other fieldsHigh correlation
page_url is highly correlated with location and 10 other fieldsHigh correlation
date_added is highly correlated with location and 7 other fieldsHigh correlation
property_id is highly correlated with location_id and 10 other fieldsHigh correlation
location_id is highly correlated with property_id and 13 other fieldsHigh correlation
page_url is highly correlated with property_id and 18 other fieldsHigh correlation
property_type is highly correlated with page_url and 4 other fieldsHigh correlation
price is highly correlated with page_url and 6 other fieldsHigh correlation
price_bin is highly correlated with location_id and 10 other fieldsHigh correlation
location is highly correlated with property_id and 18 other fieldsHigh correlation
locality is highly correlated with property_id and 18 other fieldsHigh correlation
latitude is highly correlated with property_id and 11 other fieldsHigh correlation
longitude is highly correlated with property_id and 15 other fieldsHigh correlation
baths is highly correlated with location_id and 14 other fieldsHigh correlation
area is highly correlated with property_id and 18 other fieldsHigh correlation
area_marla is highly correlated with page_url and 8 other fieldsHigh correlation
area_sqft is highly correlated with page_url and 8 other fieldsHigh correlation
bedrooms is highly correlated with page_url and 10 other fieldsHigh correlation
date_added is highly correlated with property_id and 17 other fieldsHigh correlation
month is highly correlated with location_id and 10 other fieldsHigh correlation
day is highly correlated with property_id and 12 other fieldsHigh correlation
agency is highly correlated with property_id and 16 other fieldsHigh correlation
agent is highly correlated with property_id and 14 other fieldsHigh correlation
agency has 25 (25.3%) missing values Missing
agent has 25 (25.3%) missing values Missing
page_url is uniformly distributed Uniform
property_id has unique values Unique
page_url has unique values Unique
baths has 17 (17.2%) zeros Zeros
bedrooms has 12 (12.1%) zeros Zeros

Reproduction

Analysis started2022-11-09 14:25:09.838781
Analysis finished2022-11-09 14:25:39.179544
Duration29.34 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

property_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3272394.384
Minimum347795
Maximum5019310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size920.0 B
2022-11-09T19:25:39.350357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum347795
5-th percentile775459.1
Q12439780
median3892037
Q34306236.5
95-th percentile4684132.2
Maximum5019310
Range4671515
Interquartile range (IQR)1866456.5

Descriptive statistics

Standard deviation1252689.148
Coefficient of variation (CV)0.3828050658
Kurtosis-0.5529985553
Mean3272394.384
Median Absolute Deviation (MAD)649686
Skewness-0.7291476877
Sum323967044
Variance1.5692301 × 1012
MonotonicityStrictly increasing
2022-11-09T19:25:39.569090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3477951
 
1.0%
43074961
 
1.0%
42495711
 
1.0%
41705521
 
1.0%
41598261
 
1.0%
41539881
 
1.0%
41116941
 
1.0%
41077151
 
1.0%
41003821
 
1.0%
40344021
 
1.0%
Other values (89)89
89.9%
ValueCountFrequency (%)
3477951
1.0%
4828921
1.0%
5559621
1.0%
5628431
1.0%
6869901
1.0%
7852891
1.0%
9830651
1.0%
9830661
1.0%
9830751
1.0%
12866431
1.0%
ValueCountFrequency (%)
50193101
1.0%
49514511
1.0%
49406291
1.0%
49075681
1.0%
47474131
1.0%
46771011
1.0%
45709881
1.0%
45609111
1.0%
45577991
1.0%
45368541
1.0%

location_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct59
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5073.858586
Minimum7
Maximum10542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size920.0 B
2022-11-09T19:25:39.773584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile74.4
Q11695.5
median3847
Q39433
95-th percentile9436
Maximum10542
Range10535
Interquartile range (IQR)7737.5

Descriptive statistics

Standard deviation3659.481259
Coefficient of variation (CV)0.721242265
Kurtosis-1.591116522
Mean5073.858586
Median Absolute Deviation (MAD)3469
Skewness0.1419269724
Sum502312
Variance13391803.08
MonotonicityNot monotonic
2022-11-09T19:25:40.011230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94359
 
9.1%
94346
 
6.1%
94336
 
6.1%
81725
 
5.1%
94364
 
4.0%
94323
 
3.0%
105423
 
3.0%
17843
 
3.0%
5143
 
3.0%
82
 
2.0%
Other values (49)55
55.6%
ValueCountFrequency (%)
71
1.0%
82
2.0%
481
1.0%
691
1.0%
751
1.0%
1542
2.0%
3731
1.0%
3771
1.0%
3781
1.0%
4961
1.0%
ValueCountFrequency (%)
105423
 
3.0%
97471
 
1.0%
94364
4.0%
94359
9.1%
94346
6.1%
94336
6.1%
94323
 
3.0%
84441
 
1.0%
84251
 
1.0%
81725
5.1%

page_url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size920.0 B
https://www.zameen.com/Property/lahore_model_town_6_kanal_excellent_house_for_sale_in_model_town-347795-8-1.html
 
1
https://www.zameen.com/Property/bahria_town_bahria_town_sector_c_vip_class_10_marla_brand_new_house_for_sale_in_bahria_town_lahore_in_sector_c-4307496-1784-1.html
 
1
https://www.zameen.com/Property/gulberg_gulberg_4_exclusive_location_house_for_sale-4249571-3847-1.html
 
1
https://www.zameen.com/Property/allama_iqbal_town_allama_iqbal_town_pak_block_2_marla_double_storey_house_for_sale-4170552-3527-1.html
 
1
https://www.zameen.com/Property/allama_iqbal_town_allama_iqbal_town_pak_block_2_marla_house_is_available_for_sale-4159826-3527-1.html
 
1
Other values (94)
94 

Length

Max length165
Median length126
Mean length125.2424242
Min length88

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)100.0%

Sample

1st rowhttps://www.zameen.com/Property/lahore_model_town_6_kanal_excellent_house_for_sale_in_model_town-347795-8-1.html
2nd rowhttps://www.zameen.com/Property/lahore_multan_road_1_kanal_house_for_sale-482892-48-1.html
3rd rowhttps://www.zameen.com/Property/eden_eden_avenue_9_marla_house_for_sale-555962-75-1.html
4th rowhttps://www.zameen.com/Property/gulberg_2_gulberg_2_block_s_semi_commercial_house_for_sale_near_mm_alam_price_negotiable-562843-3821-1.html
5th rowhttps://www.zameen.com/Property/allama_iqbal_town_allama_iqbal_town_raza_block_11_marla_house_for_sale_at_best_location_with_noc_ready-686990-3522-1.html

Common Values

ValueCountFrequency (%)
https://www.zameen.com/Property/lahore_model_town_6_kanal_excellent_house_for_sale_in_model_town-347795-8-1.html1
 
1.0%
https://www.zameen.com/Property/bahria_town_bahria_town_sector_c_vip_class_10_marla_brand_new_house_for_sale_in_bahria_town_lahore_in_sector_c-4307496-1784-1.html1
 
1.0%
https://www.zameen.com/Property/gulberg_gulberg_4_exclusive_location_house_for_sale-4249571-3847-1.html1
 
1.0%
https://www.zameen.com/Property/allama_iqbal_town_allama_iqbal_town_pak_block_2_marla_double_storey_house_for_sale-4170552-3527-1.html1
 
1.0%
https://www.zameen.com/Property/allama_iqbal_town_allama_iqbal_town_pak_block_2_marla_house_is_available_for_sale-4159826-3527-1.html1
 
1.0%
https://www.zameen.com/Property/bahria_town_sector_c_bahria_town_tulip_block_10_marla_brand_new_house_in_sector_c_bahria_town_lahore-4153988-1789-1.html1
 
1.0%
https://www.zameen.com/Property/dha_phase_5_dha_phase_5_block_a_1_kanal_beautiful_bungalow_house_for_sale-4111694-1598-1.html1
 
1.0%
https://www.zameen.com/Property/dha_defence_dha_phase_6_1_kanal_brand_new_beautiful_bungalow_for_sale-4107715-1448-1.html1
 
1.0%
https://www.zameen.com/Property/askari_10_askari_10_sector_f_17_marla_corner_brand_new_brig_house_available_for_sale-4100382-10542-1.html1
 
1.0%
https://www.zameen.com/Property/lahore_samanabad_house_in_samanabad_for_sale-4034402-377-1.html1
 
1.0%
Other values (89)89
89.9%

Length

2022-11-09T19:25:40.259919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.zameen.com/property/lahore_model_town_6_kanal_excellent_house_for_sale_in_model_town-347795-8-1.html1
 
1.0%
https://www.zameen.com/property/askari_10_askari_10_sector_c_10_marla_haider_design_is_available_for_sale-2439785-9434-1.html1
 
1.0%
https://www.zameen.com/property/eden_eden_avenue_9_marla_house_for_sale-555962-75-1.html1
 
1.0%
https://www.zameen.com/property/gulberg_2_gulberg_2_block_s_semi_commercial_house_for_sale_near_mm_alam_price_negotiable-562843-3821-1.html1
 
1.0%
https://www.zameen.com/property/allama_iqbal_town_allama_iqbal_town_raza_block_11_marla_house_for_sale_at_best_location_with_noc_ready-686990-3522-1.html1
 
1.0%
https://www.zameen.com/property/gulberg_paf_falcon_complex_matz_service_offer_1_kanal_house_for_sale-785289-3102-1.html1
 
1.0%
https://www.zameen.com/property/eme_society_eme_society_block_e_house_for_sale-983065-3749-1.html1
 
1.0%
https://www.zameen.com/property/eme_society_eme_society_block_a_house_for_sale-983066-3745-1.html1
 
1.0%
https://www.zameen.com/property/izmir_town_izmir_town_block_q_house_for_sale-983075-3931-1.html1
 
1.0%
https://www.zameen.com/property/eden_eden_palace_villas_7_5_marla_luxury_house_is_available_for_sale-1286643-3733-1.html1
 
1.0%
Other values (89)89
89.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

property_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size920.0 B
House
97 
Flat
 
2

Length

Max length5
Median length5
Mean length4.97979798
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHouse
2nd rowHouse
3rd rowHouse
4th rowHouse
5th rowHouse

Common Values

ValueCountFrequency (%)
House97
98.0%
Flat2
 
2.0%

Length

2022-11-09T19:25:40.479483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-09T19:25:40.563651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
house97
98.0%
flat2
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct66
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77047474.75
Minimum3200000
Maximum1250000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size920.0 B
2022-11-09T19:25:40.682804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3200000
5-th percentile7530000
Q120750000
median23500000
Q349750000
95-th percentile362000000
Maximum1250000000
Range1246800000
Interquartile range (IQR)29000000

Descriptive statistics

Standard deviation189835518.7
Coefficient of variation (CV)2.46387723
Kurtosis27.08201053
Mean77047474.75
Median Absolute Deviation (MAD)11500000
Skewness5.02013655
Sum7627700000
Variance3.603752415 × 1016
MonotonicityNot monotonic
2022-11-09T19:25:40.917163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400000005
 
5.1%
230000005
 
5.1%
225000005
 
5.1%
215000004
 
4.0%
235000004
 
4.0%
220000003
 
3.0%
500000003
 
3.0%
350000003
 
3.0%
210000003
 
3.0%
175000002
 
2.0%
Other values (56)62
62.6%
ValueCountFrequency (%)
32000002
2.0%
55000002
2.0%
60000001
1.0%
77000001
1.0%
80000001
1.0%
85000001
1.0%
95000001
1.0%
118000001
1.0%
120000002
2.0%
125000001
1.0%
ValueCountFrequency (%)
12500000001
1.0%
12000000001
1.0%
6200000001
1.0%
4800000001
1.0%
3800000001
1.0%
3600000001
1.0%
2200000001
1.0%
1800000001
1.0%
1600000001
1.0%
1250000001
1.0%

price_bin
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size920.0 B
Very High
43 
High
41 
Low
Medium

Length

Max length9
Median length4
Mean length6.202020202
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery High
2nd rowVery High
3rd rowLow
4th rowVery High
5th rowHigh

Common Values

ValueCountFrequency (%)
Very High43
43.4%
High41
41.4%
Low9
 
9.1%
Medium6
 
6.1%

Length

2022-11-09T19:25:41.101671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-09T19:25:41.200451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
high84
59.2%
very43
30.3%
low9
 
6.3%
medium6
 
4.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

location
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Memory size920.0 B
Askari
31 
Gulberg
13 
DHA Defence
10 
Bahria Town
EME Society
Other values (25)
34 

Length

Max length36
Median length9
Mean length9.575757576
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)19.2%

Sample

1st rowModel Town
2nd rowMultan Road
3rd rowEden
4th rowGulberg
5th rowAllama Iqbal Town

Common Values

ValueCountFrequency (%)
Askari31
31.3%
Gulberg13
13.1%
DHA Defence10
 
10.1%
Bahria Town6
 
6.1%
EME Society5
 
5.1%
Allama Iqbal Town4
 
4.0%
Upper Mall3
 
3.0%
Valencia Housing Society2
 
2.0%
Paragon City2
 
2.0%
Model Town2
 
2.0%
Other values (20)21
21.2%

Length

2022-11-09T19:25:41.330802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
askari31
19.6%
town16
 
10.1%
gulberg13
 
8.2%
dha10
 
6.3%
defence10
 
6.3%
society8
 
5.1%
bahria6
 
3.8%
eme5
 
3.2%
allama4
 
2.5%
iqbal4
 
2.5%
Other values (35)51
32.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

city
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size920.0 B
Lahore
99 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLahore
2nd rowLahore
3rd rowLahore
4th rowLahore
5th rowLahore

Common Values

ValueCountFrequency (%)
Lahore99
100.0%

Length

2022-11-09T19:25:41.461518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-09T19:25:41.555267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
lahore99
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

province_name
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size920.0 B
Punjab
99 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPunjab
2nd rowPunjab
3rd rowPunjab
4th rowPunjab
5th rowPunjab

Common Values

ValueCountFrequency (%)
Punjab99
100.0%

Length

2022-11-09T19:25:41.655394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-09T19:25:41.733672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
punjab99
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

locality
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Memory size920.0 B
Askari, Lahore, Punjab
31 
Gulberg, Lahore, Punjab
13 
DHA Defence, Lahore, Punjab
10 
Bahria Town, Lahore, Punjab
EME Society, Lahore, Punjab
Other values (25)
34 

Length

Max length52
Median length25
Mean length25.57575758
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)19.2%

Sample

1st rowModel Town, Lahore, Punjab
2nd rowMultan Road, Lahore, Punjab
3rd rowEden, Lahore, Punjab
4th rowGulberg, Lahore, Punjab
5th rowAllama Iqbal Town, Lahore, Punjab

Common Values

ValueCountFrequency (%)
Askari, Lahore, Punjab31
31.3%
Gulberg, Lahore, Punjab13
13.1%
DHA Defence, Lahore, Punjab10
 
10.1%
Bahria Town, Lahore, Punjab6
 
6.1%
EME Society, Lahore, Punjab5
 
5.1%
Allama Iqbal Town, Lahore, Punjab4
 
4.0%
Upper Mall, Lahore, Punjab3
 
3.0%
Valencia Housing Society, Lahore, Punjab2
 
2.0%
Paragon City, Lahore, Punjab2
 
2.0%
Model Town, Lahore, Punjab2
 
2.0%
Other values (20)21
21.2%

Length

2022-11-09T19:25:41.827416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lahore99
27.8%
punjab99
27.8%
askari31
 
8.7%
town16
 
4.5%
gulberg13
 
3.7%
dha10
 
2.8%
defence10
 
2.8%
society8
 
2.2%
bahria6
 
1.7%
eme5
 
1.4%
Other values (37)59
16.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

latitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct61
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.50025706
Minimum31.37108
Maximum31.71727872
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size920.0 B
2022-11-09T19:25:42.186765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum31.37108
5-th percentile31.3809768
Q131.466227
median31.521776
Q331.536068
95-th percentile31.5528568
Maximum31.71727872
Range0.3461987155
Interquartile range (IQR)0.069841

Descriptive statistics

Standard deviation0.05707246082
Coefficient of variation (CV)0.001811809367
Kurtosis1.504098145
Mean31.50025706
Median Absolute Deviation (MAD)0.020338
Skewness-0.2038244183
Sum3118.525449
Variance0.003257265784
MonotonicityNot monotonic
2022-11-09T19:25:42.379567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.5331618
 
8.1%
31.5374586
 
6.1%
31.533275
 
5.1%
31.4691555
 
5.1%
31.5360684
 
4.0%
31.5372393
 
3.0%
31.5421143
 
3.0%
31.5338493
 
3.0%
31.3741953
 
3.0%
31.4377442
 
2.0%
Other values (51)57
57.6%
ValueCountFrequency (%)
31.371081
 
1.0%
31.3741953
3.0%
31.3744141
 
1.0%
31.3817061
 
1.0%
31.4000961
 
1.0%
31.4025131
 
1.0%
31.405371
 
1.0%
31.4091061
 
1.0%
31.4315931
 
1.0%
31.4346681
 
1.0%
ValueCountFrequency (%)
31.717278721
 
1.0%
31.5972341
 
1.0%
31.5902341
 
1.0%
31.574430551
 
1.0%
31.5679121
 
1.0%
31.5511841
 
1.0%
31.549135211
 
1.0%
31.5434311
 
1.0%
31.5421143
3.0%
31.5392061
 
1.0%

longitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct62
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.35381822
Minimum74.177749
Maximum74.474513
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size920.0 B
2022-11-09T19:25:42.603267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum74.177749
5-th percentile74.191482
Q174.29842
median74.355898
Q374.41822
95-th percentile74.470553
Maximum74.474513
Range0.296764
Interquartile range (IQR)0.1198

Descriptive statistics

Standard deviation0.08504638763
Coefficient of variation (CV)0.001143806595
Kurtosis-0.663607522
Mean74.35381822
Median Absolute Deviation (MAD)0.058036
Skewness-0.6767585732
Sum7361.028004
Variance0.007232888048
MonotonicityNot monotonic
2022-11-09T19:25:42.799119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.4194818
 
8.1%
74.4133236
 
6.1%
74.4705535
 
5.1%
74.413585
 
5.1%
74.4086664
 
4.0%
74.3558983
 
3.0%
74.4096333
 
3.0%
74.4202113
 
3.0%
74.1914823
 
3.0%
74.3482022
 
2.0%
Other values (52)57
57.6%
ValueCountFrequency (%)
74.1777491
 
1.0%
74.179981
 
1.0%
74.1816621
 
1.0%
74.1903161
 
1.0%
74.1914823
3.0%
74.1952941
 
1.0%
74.2009311
 
1.0%
74.2096852
2.0%
74.213492
2.0%
74.2140051
 
1.0%
ValueCountFrequency (%)
74.4745131
 
1.0%
74.4705535
5.1%
74.4561841
 
1.0%
74.455991911
 
1.0%
74.4513421
 
1.0%
74.4459061
 
1.0%
74.443696331
 
1.0%
74.4400121
 
1.0%
74.4295691
 
1.0%
74.4271371
 
1.0%

baths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.313131313
Minimum0
Maximum8
Zeros17
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size920.0 B
2022-11-09T19:25:42.963508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5
median5
Q36
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.319599124
Coefficient of variation (CV)0.5377993285
Kurtosis-0.3894685051
Mean4.313131313
Median Absolute Deviation (MAD)1
Skewness-0.8205742852
Sum427
Variance5.380540095
MonotonicityNot monotonic
2022-11-09T19:25:43.088513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
528
28.3%
622
22.2%
017
17.2%
412
12.1%
79
 
9.1%
34
 
4.0%
24
 
4.0%
83
 
3.0%
ValueCountFrequency (%)
017
17.2%
24
 
4.0%
34
 
4.0%
412
12.1%
528
28.3%
622
22.2%
79
 
9.1%
83
 
3.0%
ValueCountFrequency (%)
83
 
3.0%
79
 
9.1%
622
22.2%
528
28.3%
412
12.1%
34
 
4.0%
24
 
4.0%
017
17.2%

area
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Memory size920.0 B
10 Marla
33 
1 Kanal
22 
2 Kanal
3 Marla
 
3
12 Marla
 
3
Other values (25)
33 

Length

Max length9
Median length8
Mean length7.676767677
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)18.2%

Sample

1st row6 Kanal
2nd row1 Kanal
3rd row9 Marla
4th row1 Kanal
5th row11 Marla

Common Values

ValueCountFrequency (%)
10 Marla33
33.3%
1 Kanal22
22.2%
2 Kanal5
 
5.1%
3 Marla3
 
3.0%
12 Marla3
 
3.0%
17 Marla3
 
3.0%
2 Marla2
 
2.0%
5 Marla2
 
2.0%
11 Marla2
 
2.0%
8 Kanal2
 
2.0%
Other values (20)22
22.2%

Length

2022-11-09T19:25:43.229113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
marla61
30.8%
kanal38
19.2%
1034
17.2%
122
 
11.1%
27
 
3.5%
173
 
1.5%
83
 
1.5%
123
 
1.5%
33
 
1.5%
52
 
1.0%
Other values (17)22
 
11.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

area_marla
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.86262626
Minimum2
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size920.0 B
2022-11-09T19:25:43.394904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q110
median10
Q320
95-th percentile96.6
Maximum200
Range198
Interquartile range (IQR)10

Descriptive statistics

Standard deviation33.74980147
Coefficient of variation (CV)1.476199675
Kurtosis13.22936034
Mean22.86262626
Median Absolute Deviation (MAD)6
Skewness3.591827445
Sum2263.4
Variance1139.049099
MonotonicityNot monotonic
2022-11-09T19:25:43.578933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1033
33.3%
2022
22.2%
405
 
5.1%
33
 
3.0%
123
 
3.0%
173
 
3.0%
22
 
2.0%
52
 
2.0%
112
 
2.0%
1602
 
2.0%
Other values (20)22
22.2%
ValueCountFrequency (%)
22
2.0%
2.51
 
1.0%
33
3.0%
41
 
1.0%
52
2.0%
5.51
 
1.0%
61
 
1.0%
71
 
1.0%
7.51
 
1.0%
81
 
1.0%
ValueCountFrequency (%)
2001
 
1.0%
1602
 
2.0%
1301
 
1.0%
1201
 
1.0%
941
 
1.0%
801
 
1.0%
405
5.1%
321
 
1.0%
241
 
1.0%
222
 
2.0%

area_sqft
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6224.372525
Minimum544.5
Maximum54450.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size920.0 B
2022-11-09T19:25:43.750795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum544.5
5-th percentile816.75
Q12722.51
median2722.51
Q35445.02
95-th percentile26299.443
Maximum54450.2
Range53905.7
Interquartile range (IQR)2722.51

Descriptive statistics

Standard deviation9188.417241
Coefficient of variation (CV)1.476199762
Kurtosis13.22936044
Mean6224.372525
Median Absolute Deviation (MAD)1633.51
Skewness3.591827422
Sum616212.88
Variance84427011.4
MonotonicityNot monotonic
2022-11-09T19:25:43.929506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2722.5133
33.3%
5445.0222
22.2%
10890.045
 
5.1%
816.753
 
3.0%
3267.013
 
3.0%
4628.273
 
3.0%
544.52
 
2.0%
1361.252
 
2.0%
2994.762
 
2.0%
43560.162
 
2.0%
Other values (20)22
22.2%
ValueCountFrequency (%)
544.52
2.0%
680.631
 
1.0%
816.753
3.0%
10891
 
1.0%
1361.252
2.0%
1497.381
 
1.0%
1633.511
 
1.0%
1905.761
 
1.0%
2041.881
 
1.0%
2178.011
 
1.0%
ValueCountFrequency (%)
54450.21
 
1.0%
43560.162
 
2.0%
35392.631
 
1.0%
32670.121
 
1.0%
25591.591
 
1.0%
21780.081
 
1.0%
10890.045
5.1%
8712.031
 
1.0%
6534.021
 
1.0%
5989.522
 
2.0%

purpose
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size920.0 B
For Sale
99 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFor Sale
2nd rowFor Sale
3rd rowFor Sale
4th rowFor Sale
5th rowFor Sale

Common Values

ValueCountFrequency (%)
For Sale99
100.0%

Length

2022-11-09T19:25:44.079266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-09T19:25:44.178042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
for99
50.0%
sale99
50.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

bedrooms
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.01010101
Minimum0
Maximum8
Zeros12
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size920.0 B
2022-11-09T19:25:44.240551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5
median4
Q35
95-th percentile6
Maximum8
Range8
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.854365778
Coefficient of variation (CV)0.4624237079
Kurtosis0.6904686535
Mean4.01010101
Median Absolute Deviation (MAD)1
Skewness-0.818601548
Sum397
Variance3.438672439
MonotonicityNot monotonic
2022-11-09T19:25:44.383300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
531
31.3%
429
29.3%
012
 
12.1%
611
 
11.1%
310
 
10.1%
83
 
3.0%
23
 
3.0%
ValueCountFrequency (%)
012
 
12.1%
23
 
3.0%
310
 
10.1%
429
29.3%
531
31.3%
611
 
11.1%
83
 
3.0%
ValueCountFrequency (%)
83
 
3.0%
611
 
11.1%
531
31.3%
429
29.3%
310
 
10.1%
23
 
3.0%
012
 
12.1%

date_added
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct23
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Memory size920.0 B
06-18-2019
32 
07-03-2019
11 
05-03-2019
10 
04-04-2019
06-25-2019
Other values (18)
30 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)12.1%

Sample

1st row07-17-2019
2nd row10-06-2018
3rd row07-03-2019
4th row04-04-2019
5th row04-04-2019

Common Values

ValueCountFrequency (%)
06-18-201932
32.3%
07-03-201911
 
11.1%
05-03-201910
 
10.1%
04-04-20199
 
9.1%
06-25-20197
 
7.1%
04-03-20196
 
6.1%
06-11-20193
 
3.0%
02-03-20193
 
3.0%
06-02-20192
 
2.0%
06-30-20192
 
2.0%
Other values (13)14
14.1%

Length

2022-11-09T19:25:44.555166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
06-18-201932
32.3%
07-03-201911
 
11.1%
05-03-201910
 
10.1%
04-04-20199
 
9.1%
06-25-20197
 
7.1%
04-03-20196
 
6.1%
06-11-20193
 
3.0%
02-03-20193
 
3.0%
06-02-20192
 
2.0%
06-30-20192
 
2.0%
Other values (13)14
14.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.959596
Minimum2018
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size920.0 B
2022-11-09T19:25:44.692385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2018
5-th percentile2019
Q12019
median2019
Q32019
95-th percentile2019
Maximum2019
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1979070035
Coefficient of variation (CV)9.802425163 × 10-5
Kurtosis20.89246473
Mean2018.959596
Median Absolute Deviation (MAD)0
Skewness-4.740329111
Sum199877
Variance0.03916718202
MonotonicityNot monotonic
2022-11-09T19:25:44.810979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
201995
96.0%
20184
 
4.0%
ValueCountFrequency (%)
20184
 
4.0%
201995
96.0%
ValueCountFrequency (%)
201995
96.0%
20184
 
4.0%

month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.666666667
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size920.0 B
2022-11-09T19:25:44.947743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q36
95-th percentile7
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.778557085
Coefficient of variation (CV)0.3138630151
Kurtosis3.700743963
Mean5.666666667
Median Absolute Deviation (MAD)1
Skewness0.5306536478
Sum561
Variance3.163265306
MonotonicityNot monotonic
2022-11-09T19:25:45.088343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
648
48.5%
715
 
15.2%
415
 
15.2%
510
 
10.1%
24
 
4.0%
12
 
2.0%
122
 
2.0%
101
 
1.0%
31
 
1.0%
111
 
1.0%
ValueCountFrequency (%)
12
 
2.0%
24
 
4.0%
31
 
1.0%
415
 
15.2%
510
 
10.1%
648
48.5%
715
 
15.2%
101
 
1.0%
111
 
1.0%
122
 
2.0%
ValueCountFrequency (%)
122
 
2.0%
111
 
1.0%
101
 
1.0%
715
 
15.2%
648
48.5%
510
 
10.1%
415
 
15.2%
31
 
1.0%
24
 
4.0%
12
 
2.0%

day
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.25252525
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size920.0 B
2022-11-09T19:25:45.415950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median10
Q318
95-th percentile25
Maximum30
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.438666393
Coefficient of variation (CV)0.7499353437
Kurtosis-1.319764034
Mean11.25252525
Median Absolute Deviation (MAD)7
Skewness0.3691181431
Sum1114
Variance71.2110905
MonotonicityNot monotonic
2022-11-09T19:25:45.563575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1833
33.3%
330
30.3%
411
 
11.1%
257
 
7.1%
54
 
4.0%
113
 
3.0%
22
 
2.0%
302
 
2.0%
171
 
1.0%
61
 
1.0%
Other values (5)5
 
5.1%
ValueCountFrequency (%)
11
 
1.0%
22
 
2.0%
330
30.3%
411
 
11.1%
54
 
4.0%
61
 
1.0%
101
 
1.0%
113
 
3.0%
121
 
1.0%
171
 
1.0%
ValueCountFrequency (%)
302
 
2.0%
261
 
1.0%
257
 
7.1%
221
 
1.0%
1833
33.3%
171
 
1.0%
121
 
1.0%
113
 
3.0%
101
 
1.0%
61
 
1.0%

agency
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct16
Distinct (%)21.6%
Missing25
Missing (%)25.3%
Memory size920.0 B
Forces Properties
31 
Punjaab Estates
12 
Vital Estate
Hamza Real Estate
Sukhera Estate & Builders
 
3
Other values (11)
12 

Length

Max length28
Median length17
Mean length16.32432432
Min length11

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)13.5%

Sample

1st rowReal Biz International
2nd rowKhan Estate
3rd rowShahum Estate 2
4th rowMATZ Services
5th rowSukhera Estate & Builders

Common Values

ValueCountFrequency (%)
Forces Properties31
31.3%
Punjaab Estates12
 
12.1%
Vital Estate9
 
9.1%
Hamza Real Estate7
 
7.1%
Sukhera Estate & Builders3
 
3.0%
Valo Marketing2
 
2.0%
Real Biz International1
 
1.0%
Rana Estate Agency1
 
1.0%
The Property Lounge1
 
1.0%
Jinnah Associates1
 
1.0%
Other values (6)6
 
6.1%
(Missing)25
25.3%

Length

2022-11-09T19:25:45.782294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
forces31
18.5%
properties31
18.5%
estate24
14.3%
punjaab12
 
7.1%
estates12
 
7.1%
vital9
 
5.4%
real9
 
5.4%
hamza7
 
4.2%
4
 
2.4%
builders4
 
2.4%
Other values (19)25
14.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

agent
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct17
Distinct (%)23.0%
Missing25
Missing (%)25.3%
Memory size920.0 B
Khalid Saeed Khan
31 
Irfan Rehman Khan
12 
Shahbaz Mughal
Imran Shahad
Ahmed Sheraz Sukhera
 
3
Other values (12)
12 

Length

Max length45
Median length17
Mean length16.7972973
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)16.2%

Sample

1st rowUsama Khan
2nd rowmohsinkhan and B
3rd rowBabar Hameed, Raja Omar
4th rowGroup Captain (R) Tajammul Baig
5th rowAhmed Sheraz Sukhera

Common Values

ValueCountFrequency (%)
Khalid Saeed Khan31
31.3%
Irfan Rehman Khan12
 
12.1%
Shahbaz Mughal9
 
9.1%
Imran Shahad7
 
7.1%
Ahmed Sheraz Sukhera3
 
3.0%
Nabeel Khalid Ch M Naveed Muhammad Bin Shahid1
 
1.0%
Rana Junaid1
 
1.0%
Najam Ul Saqib1
 
1.0%
Nabeel Khalid Muhammad Bin Shahid1
 
1.0%
Shafiq Malik1
 
1.0%
Other values (7)7
 
7.1%
(Missing)25
25.3%

Length

2022-11-09T19:25:45.950744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
khan44
20.8%
khalid33
15.6%
saeed31
14.6%
irfan12
 
5.7%
rehman12
 
5.7%
shahbaz9
 
4.2%
mughal9
 
4.2%
imran7
 
3.3%
shahad7
 
3.3%
ahmed3
 
1.4%
Other values (35)45
21.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-11-09T19:25:34.856258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:16.956901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:18.604289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:20.075992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:21.734885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:23.271502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:24.912349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:26.441136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:28.127997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:29.597412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:31.258650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:33.259310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:34.972729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:17.119310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:18.713094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:20.187511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:21.853125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:23.391972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:25.035848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:26.557012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:28.240466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:29.720613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:31.421800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:33.373259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:35.091155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:17.227058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:18.831331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:20.308459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:21.977467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:23.514350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:25.156965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:26.681022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:28.352001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:29.843155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:31.609579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:33.497315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:35.216179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:17.343460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:18.953048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:20.442907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:22.112982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:23.636732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:25.284979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:26.813339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:28.488061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:29.978975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:31.787091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:33.616719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:35.339370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:17.461997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:19.080641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:20.571698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:22.240989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:23.758769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:25.414772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:26.943729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:28.609958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:30.104934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:32.212188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:33.738998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:35.468484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:17.582042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:19.195379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:20.697123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:22.367680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:23.880564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:25.546159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:27.071880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:28.729524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:30.231731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:32.373093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:33.857115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:35.588619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:17.703182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:19.321924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:20.826909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:22.510819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:24.005967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:25.675305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:27.209277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:28.855925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:30.361950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:32.504815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:33.983837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:35.725838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:17.830727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:19.465992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:20.965337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:22.638922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:24.138741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:25.809464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:27.344342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:28.985955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:30.650138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:32.639590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:34.256814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:35.880097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:17.946118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:19.584059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:21.086549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:22.764727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:24.271811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:25.935728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:27.617449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:29.109314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:30.754313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:32.761946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:34.372287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:36.078810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:18.217661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:19.709843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:21.369659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:22.895323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:24.565481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:26.063751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:27.742406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:29.226756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:30.889031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:32.890234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:34.502756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:36.368847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:18.375712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:19.831522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:21.489187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:23.020236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:24.674597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:26.187134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:27.873463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:29.346702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:31.017118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:33.016073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:34.624000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:36.790012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:18.491011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:19.951233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:21.609958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:23.149493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:24.792400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:26.311800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:27.997800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:29.479342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:31.139164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:33.142375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T19:25:34.738418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-11-09T19:25:46.106985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-09T19:25:46.372608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-09T19:25:46.655817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-09T19:25:46.937048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-09T19:25:47.195669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-09T19:25:37.321060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-09T19:25:38.001961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-09T19:25:38.448887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-09T19:25:38.888601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

property_idlocation_idpage_urlproperty_typepriceprice_binlocationcityprovince_namelocalitylatitudelongitudebathsareaarea_marlaarea_sqftpurposebedroomsdate_addedyearmonthdayagencyagent
03477958https://www.zameen.com/Property/lahore_model_town_6_kanal_excellent_house_for_sale_in_model_town-347795-8-1.htmlHouse220000000Very HighModel TownLahorePunjabModel Town, Lahore, Punjab31.48386974.32568606 Kanal120.032670.12For Sale007-17-20192019717Real Biz InternationalUsama Khan
148289248https://www.zameen.com/Property/lahore_multan_road_1_kanal_house_for_sale-482892-48-1.htmlHouse40000000Very HighMultan RoadLahorePunjabMultan Road, Lahore, Punjab31.43159374.17998051 Kanal20.05445.02For Sale510-06-20182018106Khan Estatemohsinkhan and B
255596275https://www.zameen.com/Property/eden_eden_avenue_9_marla_house_for_sale-555962-75-1.htmlHouse9500000LowEdenLahorePunjabEden, Lahore, Punjab31.49934874.41695909 Marla9.02450.26For Sale307-03-2019201973Shahum Estate 2Babar Hameed, Raja Omar
35628433821https://www.zameen.com/Property/gulberg_2_gulberg_2_block_s_semi_commercial_house_for_sale_near_mm_alam_price_negotiable-562843-3821-1.htmlHouse125000000Very HighGulbergLahorePunjabGulberg, Lahore, Punjab31.52206974.35551271 Kanal20.05445.02For Sale804-04-2019201944NaNNaN
46869903522https://www.zameen.com/Property/allama_iqbal_town_allama_iqbal_town_raza_block_11_marla_house_for_sale_at_best_location_with_noc_ready-686990-3522-1.htmlHouse21000000HighAllama Iqbal TownLahorePunjabAllama Iqbal Town, Lahore, Punjab31.50648374.286017511 Marla11.02994.76For Sale604-04-2019201944NaNNaN
57852893102https://www.zameen.com/Property/gulberg_paf_falcon_complex_matz_service_offer_1_kanal_house_for_sale-785289-3102-1.htmlHouse52000000Very HighGulbergLahorePunjabGulberg, Lahore, Punjab31.49590974.35056961 Kanal20.05445.02For Sale506-02-2019201962MATZ ServicesGroup Captain (R) Tajammul Baig
69830653749https://www.zameen.com/Property/eme_society_eme_society_block_e_house_for_sale-983065-3749-1.htmlHouse32500000HighEME SocietyLahorePunjabEME Society, Lahore, Punjab31.43997874.20968501 Kanal20.05445.02For Sale507-03-2019201973Sukhera Estate & BuildersAhmed Sheraz Sukhera
79830663745https://www.zameen.com/Property/eme_society_eme_society_block_a_house_for_sale-983066-3745-1.htmlHouse31500000HighEME SocietyLahorePunjabEME Society, Lahore, Punjab31.43774474.21349001 Kanal20.05445.02For Sale607-03-2019201973Sukhera Estate & BuildersAhmed Sheraz Sukhera
89830753931https://www.zameen.com/Property/izmir_town_izmir_town_block_q_house_for_sale-983075-3931-1.htmlHouse40000000Very HighIzmir TownLahorePunjabIzmir Town, Lahore, Punjab31.40910674.18166201.6 Kanal32.08712.03For Sale607-03-2019201973Sukhera Estate & BuildersAhmed Sheraz Sukhera
912866433733https://www.zameen.com/Property/eden_eden_palace_villas_7_5_marla_luxury_house_is_available_for_sale-1286643-3733-1.htmlHouse13500000MediumEdenLahorePunjabEden, Lahore, Punjab31.44111374.23968347.5 Marla7.52041.88For Sale404-04-2019201944NaNNaN

Last rows

property_idlocation_idpage_urlproperty_typepriceprice_binlocationcityprovince_namelocalitylatitudelongitudebathsareaarea_marlaarea_sqftpurposebedroomsdate_addedyearmonthdayagencyagent
8945368543852https://www.zameen.com/Property/gulberg_main_boulevard_gulberg_commercialized_piece_of_property_house_for_sale-4536854-3852-1.htmlHouse1200000000Very HighGulbergLahorePunjabGulberg, Lahore, Punjab31.51943574.34554808 Kanal160.043560.16For Sale004-04-2019201944Punjaab EstatesIrfan Rehman Khan
9045577993850https://www.zameen.com/Property/gulberg_mm_alam_road_shopping_paradise_of_lahore_house_for_sale-4557799-3850-1.htmlHouse380000000Very HighGulbergLahorePunjabGulberg, Lahore, Punjab31.51047174.35052602 Kanal40.010890.04For Sale005-03-2019201953Punjaab EstatesIrfan Rehman Khan
914560911496https://www.zameen.com/Property/mozang_mozang_chungi_al_qader_center_ground_floor_new_apartment_for_sale-4560911-496-1.htmlFlat3200000LowMozangLahorePunjabMozang, Lahore, Punjab31.54913574.31511723 Marla3.0816.75For Sale207-03-2019201973NaNNaN
9245709881447https://www.zameen.com/Property/dha_defence_dha_phase_5_original_faisal_rasool_brand_new_classical_bungalow-4570988-1447-1.htmlHouse59800000Very HighDHA DefenceLahorePunjabDHA Defence, Lahore, Punjab31.46249374.40934271 Kanal20.05445.02For Sale605-03-2019201953Harum Real Estate & BuildersTayyab Khurshad
9346771018172https://www.zameen.com/Property/dha_defence_defence_raya_dha_raya_2_kanal_facing_golf_course_fully_basement_house_for_sale-4677101-8172-1.htmlHouse66000000Very HighDHA DefenceLahorePunjabDHA Defence, Lahore, Punjab31.46915574.47055362 Kanal40.010890.04For Sale606-25-20192019625Vital EstateShahbaz Mughal
944747413154https://www.zameen.com/Property/gulberg_zafar_ali_road_6_kanal_house_for_sale_on_zafar_ali_road_mall_road_upper_mall_lahore_excellent_location-4747413-154-1.htmlHouse360000000Very HighGulbergLahorePunjabGulberg, Lahore, Punjab31.53842074.352357710 Kanal200.054450.20For Sale506-30-20192019630Hamza Real EstateImran Shahad
954907568514https://www.zameen.com/Property/lahore_upper_mall_4_kanal__house_for_sale_upper_mall_lahore-4907568-514-1.htmlHouse160000000Very HighUpper MallLahorePunjabUpper Mall, Lahore, Punjab31.54211474.35589861 Kanal20.05445.02For Sale501-18-20192019118Hamza Real EstateImran Shahad
9649406298https://www.zameen.com/Property/lahore_model_town_3_marla_house_for_sale-4940629-8-1.htmlHouse8000000LowModel TownLahorePunjabModel Town, Lahore, Punjab31.47388474.32908033 Marla3.0816.75For Sale207-03-2019201973NaNNaN
9749514517https://www.zameen.com/Property/lahore_gulberg_blue_zone_house_for_sale-4951451-7-1.htmlHouse480000000Very HighGulbergLahorePunjabGulberg, Lahore, Punjab31.52236174.34717204 Kanal80.021780.08For Sale005-03-2019201953Punjaab EstatesIrfan Rehman Khan
9850193101534https://www.zameen.com/Property/garden_town_garden_town_ahmed_block_purely_residential_apartments_for_sale-5019310-1534-1.htmlFlat22500000Very HighGarden TownLahorePunjabGarden Town, Lahore, Punjab31.50780074.31873307 Marla7.01905.76For Sale004-03-2019201943Punjaab EstatesIrfan Rehman Khan